Black Kelly - R Object-oriented Programming

Kelly Black is a faculty member in the Department of Mathematics at Clarkson University. His background is in numerical analysis with a focus on the use of spectral methods and stochastic differential equations. He makes extensive use of R in the analysis of the results of Monte-Carlo simulations.

In addition to using R for his research interests, Kelly also uses the R environment for his statistics classes. He has extensive experience sharing his experiences with R in the classroom. The use of R to explore datasets is an important part of the curriculum.

I would like to thank my wife and daughter for their support and inspiration. You are my life, and we do this together.

I also wish to thank to thank the people who took a direct role in bringing this book to completion. In particular, thanks go to the Content Development Editor, Parita Khedekar, for her work in assembling everything, keeping me on track, and ensuring the overall integrity of this book. Additional thanks go to the Technical Editor, Tanvi Bhatt, for maintaining the integrity of the book as a whole and, technical guidance. Thank you!

I would also like to thank the reviewers. Unfortunately, some of your insights were not able to be integrated into this work. I did read your reviews and valued them. I tried to balance your concerns to the best of my ability. Thank you as well.

Mzabalazo Z. Ngwenya has worked extensively in the field of statistical consulting and currently works as a biometrician. He has an MSc in Mathematical Statistics from the University of Cape Town and is presently studying for a PhD. His research interests include statistical computing, machine learning, and spatial statistics. Previously, he was involved in reviewing Packt Publishing's Learning RStudio for R Statistical Computing , R Statistical Application Development by Example Beginner's Guide , and Machine Learning with R .

Prabhanjan Tattar is currently working as a Business Analysis Senior Advisor at Dell Global Analytics, Dell. He has 7 years of experience as a statistical analyst. Survival analysis and statistical inference are his main areas of research/interest, and he has published several research papers in peer-reviewed journals and also authored two books on R: R Statistical Application Development by Example , Packt Publishing , and A Course in Statistics with R , Narosa Publishing . The R packages, gpk and RSADBE are also maintained by him.

Tengfei Yin earned his PhD in Molecular, Cellular, and Developmental Biology (MCDB) with a focus on computational biology and bioinformatics from Iowa State University, with a minor in Statistics. His research interests include information visualization, high-throughput biological data analysis, data mining, and applied statistical genetics. He has developed and maintained several software packages in R and Bioconductor.

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Preface

The R environment is a powerful software suite that started as a model for the S language originally developed at Bell Laboratories. The original code base was created by Ross Ihaka and Robert Gentleman in 1993. It rapidly grew with the help of others, and it has since become a standard in statistical computing. The software suite itself has grown well beyond an implementation of a language and has become an "environment". It is extensible, and the wide variety of packages that are available help make it a powerful resource that continues to grow in popularity and power.

Our aim in this book is to provide a resource for programming using the R language, and we assume that you will be making use of the R environment to implement and test your code. The book can be roughly divided into four parts. In the first part, we provide a discussion of the basic ideas and topics necessary to understand how R classifies data and the options that can be used to make calculations from data. In the second part, we provide a discussion of how R organizes data and the options available to keep track of data, display data, and read and save data. In the third part, we provide a discussion on programming topics specific to the R language and the options available for object-oriented programming. In the fourth part, we provide several extended examples as a way to demonstrate how all of the topics can fit together to solve problems.